In recent years, tissue profiling using IHC has assumed an increasingly important role in cancer diagnosis and treatment. Immunohistochemical staining is widely used in the identification of abnormal cells such as those found in cancerous tumors. In fact, using such staining, pathologists not only provide diagnosis but also prognostic and therapeutic prediction about the patient's tumor. IHC analysis has assumed a critical role in clarifying the diagnosis in challenging cases and resolving differential diagnoses. Moreover, IHC testing of different markers can provide information useful for planning targeted therapies in the clinical setting.
In histopathology, IHC-stained tissue sections of patient biopsies are evaluated by pathologists in order to study the presence and grade of a disease. Conventionally, pathologists examine cells and tissues under a microscope to search for the disease presence using many years of training and experience. Diagnosing a disease after manually analyzing numerous biopsy slides can be tedious and represents a labor-intensive work for pathologists. Besides, the diagnosis is affected by subjective judgment and intra- and inter-observer variability. In this respect, digital pathology aims to extract quantitative information from scanned histopathological sections to aid pathologists in the diagnostic process. Recent advances in digital pathology enables the automated recognition of relevant patterns and has the potential to provide valuable assistance to the pathologist. Researchers in the fields of pathology and image analytics have recognized the importance of quantitative analysis of pathology images. This quantitative analysis of digital pathology is important not only from a diagnostic perspective, but also to understand the underlying rationale for a specific diagnosis being rendered. Hence, it can play an important role to support pathologists' decision about the presence/absence of a biomarker being indicative of a particular disease, and also to help in stratifying disease progression.
In order to achieve accurate results in IHC staining, the selection of optimal staining parameters is key. A selection of the wrong staining parameters such as staining duration or stain concentration can result in understained tissues and false negative observations and/or in overstained tissues and false positive observations. However, the identification of the appropriate staining parameters for a particular tissue is a time consuming, tedious task. Moreover, even in case the lab practitioner intends to stain a particular tissue according to a well established staining protocol, all kinds of errors may arise before and during the staining process. Thus, a method of assessing the staining quality based on the analysis of a digital image of the stained tissue would be highly advantageous as it would allow to avoid erroneous medical diagnosis and treatment decisions resulting from a wrong manual or automated interpretation of tissue images whose staining quality is low.
Existing methods for tissue staining quality assessment aim to evaluate the quality of staining tissue sections do not allow to identify the contribution of individual staining parameters to the final staining quality (see e.g. M. Grunkin and J. D. Hansen. 2015. “Assessment of staining quality. International Patent” WO 2015/135550 A1, and Pinard, R., Tedeschi G. R., Williams C., and Wang, Donaxiao 2009: “Methods and system for validating sample images for quantitative immunoassays”, WO 2009/020972 A2). Pinard et al. presented a system for qualitative evaluation of digital images. The system extracts at least one quantitative quality indicator and compares it against respective user-defined or pre-defined minimum acceptable thresholds. The quantitative quality indicators include signal intensity and uniformity based staining quality assessment, sample sufficiency and position based tissue integrity and image focus based image integrity. Failure of one or more of the quantitative quality indicators to meet its respective threshold suggests that the sample is unsuitable for subsequent automated pathological evaluation. The method suggested by M. Grunkin et al. is based on a comparison between the staining at the working laboratory with standardized staining achieved at a standardized laboratory, and determining a quantitative quality measure describing a quantitative relation between both staining. Therefore, the method requires a reference staining produced at a standardized laboratory in order to assign a quality value to a control staining. Their measure of quality is based on features like connectivity, number of cells, number of positive and negative nuclei, Allred-score, and the like.